from timeit import timeit

_SCREAMING_SNAKE_CASE 		=      {
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())





def 			lowercase(	UpperCamelCase_       )		->	bool:







 '''simple docstring'''
 UpperCamelCase			  =      0
 UpperCamelCase			  =      len(UpperCamelCase_       ) - 1
 while start_i < end_i:
  if s[start_i] == s[end_i]:
   start_i += 1
   end_i -= 1
  else:
   return False
 return True





def 			lowercase(	UpperCamelCase_       )		->	bool:







 '''simple docstring'''
 UpperCamelCase			  =      len(UpperCamelCase_       ) // 2
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # We need to traverse till half of the length of string
 # as we can get access of the i'th last element from
 # i'th index.
 # eg: [0,1,2,3,4,5] => 4th index can be accessed
 # with the help of 1st index (i==n-i-1)
 # where n is length of string
 return all(s[i] == s[n - i - 1] for i in range(UpperCamelCase_       )       )





def 			lowercase(	UpperCamelCase_       )		->	bool:







 '''simple docstring'''
 if len(UpperCamelCase_       ) <= 2:
  return True
 if s[0] == s[len(UpperCamelCase_       ) - 1]:
  return is_palindrome_recursive(s[1:-1]       )
 else:
  return False





def 			lowercase(	UpperCamelCase_       )		->	bool:







 '''simple docstring'''
 return s == s[::-1]





def 			lowercase(	UpperCamelCase_       )		->	None:







 '''simple docstring'''
 UpperCamelCase			  =      f"""all({name}(key) is value for key, value in test_data.items())"""
 UpperCamelCase			  =      f"""from __main__ import test_data, {name}"""
 UpperCamelCase			  =      500000
 UpperCamelCase			  =      timeit(stmt=UpperCamelCase_							,					setup=UpperCamelCase_							,					number=UpperCamelCase_       )
 print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""       )


if __name__ == "__main__":
     for key, value in test_data.items():
          assert is_palindrome(key) is is_palindrome_recursive(key)
          assert is_palindrome(key) is is_palindrome_slice(key)
          print(F'''{key:21} {value}''')
     print("""a man a plan a canal panama""")

     # finished 500,000 runs in 0.46793 seconds
     benchmark_function("""is_palindrome_slice""")
     # finished 500,000 runs in 0.85234 seconds
     benchmark_function("""is_palindrome""")
     # finished 500,000 runs in 1.32028 seconds
     benchmark_function("""is_palindrome_recursive""")
     # finished 500,000 runs in 2.08679 seconds
     benchmark_function("""is_palindrome_traversal""")

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
